1105 lines
96 KiB
Plaintext
1105 lines
96 KiB
Plaintext
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c}} Z=d3„ Z>d4„ Z?d5„ Z@d6„ ZAd7„ ZBded8„ZCd9„ ZDd:„ ZEd;„ ZFd<„ eDeDeEeFd=œZGd>„ ZHd?„ ZId@„ ZJdA„ ZKdfdB„ZLejš dC„ « ZN ejž eP ej¢ dD« ¬E« fdF„ZRdG„ ZSdH„ ZTdI„ ZUdJ„ ZVdK„ ZWejš dL„ « ZX G dM„ dNej² j´ « Z[dO„ Z\dP„ Z]dQ„ Z^dR„ Z_dS„ Z`dT„ ZadU„ ZbdV„ ZcdW„ ZdejÊ dfdX„« ZfdY„ ZgdZ„ Zhd[„ Zid\„ Zjd]„ Zkd^„ Zld_„ Zmy# e$ r
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ddlmZ Y �Œëw xY wc c}} w )gz‡
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A collection of utility functions and classes. Originally, many
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(but not all) were from the Python Cookbook -- hence the name cbook.
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é N)ÚPath)ÚVisibleDeprecationWarning)Ú_apiÚ_c_internal_utilsc ó, — e Zd ZdZd„ Zed„ « Zd„ Zy)Ú_ExceptionInfoa—
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A class to carry exception information around.
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This is used to store and later raise exceptions. It's an alternative to
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directly storing Exception instances that circumvents traceback-related
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issues: caching tracebacks can keep user's objects in local namespaces
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alive indefinitely, which can lead to very surprising memory issues for
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users and result in incorrect tracebacks.
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c ó — || _ || _ y ©N©Ú_clsÚ_args)ÚselfÚclsÚargss úS/home/antigravity/intellecton/venv/lib/python3.12/site-packages/matplotlib/cbook.pyÚ__init__z_ExceptionInfo.__init__. s € ؈Œ ؈�
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r ©r s r Ú to_exceptionz_ExceptionInfo.to_exception6 s € ؈t�y‰y˜$Ÿ*™*Ð%Ð%r N)Ú__name__Ú
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__module__Ú __qualname__Ú__doc__r Ú classmethodr r © r r r r # s% „ ñòð ñ)ó ð)ó&r r c óD — t j j d« xsa t j j d« xs@ t j j d« xs t j j d« } | r| j j « ryt j j d« }|rF|j
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Return the interactive framework whose event loop is currently running, if
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any, or "headless" if no event loop can be started, or None.
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Returns
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-------
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Optional[str]
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One of the following values: "qt", "gtk3", "gtk4", "wx", "tk",
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"macosx", "headless", ``None``.
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zPyQt6.QtWidgetszPySide6.QtWidgetszPyQt5.QtWidgetszPySide2.QtWidgetsÚqtzgi.repository.Gtké r )ÚGLibÚgtk4é Úgtk3ÚwxÚtkinterÚtkzmatplotlib.backends._macosxÚmacosxÚheadlessN)ÚsysÚmodulesÚgetÚ QApplicationÚinstanceÚ
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gi.repositoryr$ Ú
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_StrongRefzU
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isinstancerJ rM )r Úothers r Ú__eq__z_StrongRef.__eq__~ s! € ܘ%¤Ó,ÒH°·±¸e¿j¹jÑ1HÐHr c ó, — t | j « S r
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A minimal dict-like class that also supports unhashable keys, storing them
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in a list of key-value pairs.
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This class only implements the interface needed for `CallbackRegistry`, and
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tries to minimize the overhead for the hashable case.
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— || j v r| j j |« S |r|d S t |« ‚# t $ r; t | j « D ] \ }\ }}||k( sŒ| j |= |c cY S Y ŒUw xY w©Nr )rc Úpopr[ ri rd rp )r rk r rm rf rg s r rt z_UnhashDict.pop³ s‹ € ð Ø�d—j‘jÑ Ø—z‘z—~‘~ cÓ*Ð*ð !ñ ؘ‘7ˆNÜ�s‹mÐøô ò Ü & t§{¡{Ó 3ò
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rb ©r rf rg s r Ú__iter__z_UnhashDict.__iter__À s4 è ø€ Ø—:‘:× Ð Ø—K‘Kò ‰DˆAˆqØ‹Gñ ð ús ‚2’0“ 2N) r r r r r rn rq rt rw r r r r` r` � s „ ñòò 1ò ò ór r` c óŽ — e Zd ZdZefddœd„Zd„ Zd„ Zd„ Zd„ Z e
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œd„« Zy)ÚCallbackRegistryaö
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Handle registering, processing, blocking, and disconnecting
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for a set of signals and callbacks:
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>>> def oneat(x):
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... print('eat', x)
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>>> def ondrink(x):
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... print('drink', x)
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>>> from matplotlib.cbook import CallbackRegistry
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>>> callbacks = CallbackRegistry()
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>>> id_eat = callbacks.connect('eat', oneat)
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>>> id_drink = callbacks.connect('drink', ondrink)
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>>> callbacks.process('drink', 123)
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drink 123
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>>> callbacks.process('eat', 456)
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eat 456
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>>> callbacks.process('be merry', 456) # nothing will be called
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>>> callbacks.disconnect(id_eat)
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>>> callbacks.process('eat', 456) # nothing will be called
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>>> with callbacks.blocked(signal='drink'):
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... callbacks.process('drink', 123) # nothing will be called
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>>> callbacks.process('drink', 123)
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drink 123
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In practice, one should always disconnect all callbacks when they are
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no longer needed to avoid dangling references (and thus memory leaks).
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However, real code in Matplotlib rarely does so, and due to its design,
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it is rather difficult to place this kind of code. To get around this,
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and prevent this class of memory leaks, we instead store weak references
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to bound methods only, so when the destination object needs to die, the
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CallbackRegistry won't keep it alive.
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Parameters
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----------
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exception_handler : callable, optional
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If not None, *exception_handler* must be a function that takes an
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`Exception` as single parameter. It gets called with any `Exception`
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raised by the callbacks during `CallbackRegistry.process`, and may
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either re-raise the exception or handle it in another manner.
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The default handler prints the exception (with `traceback.print_exc`) if
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an interactive event loop is running; it re-raises the exception if no
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interactive event loop is running.
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signals : list, optional
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If not None, *signals* is a list of signals that this registry handles:
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attempting to `process` or to `connect` to a signal not in the list
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throws a `ValueError`. The default, None, does not restrict the
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handled signals.
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Like `.connect`, but the callback is kept when pickling/unpickling.
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Disconnect the callback registered with callback id *cid*.
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No error is raised if such a callback does not exist.
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« « D ] } |« }|€Œ
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||i |¤Ž Œ y# t $ r)}| j �| j |« n‚ Y d}~ŒFd}~ww xY w)z¢
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Process signal *s*.
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All of the functions registered to receive callbacks on *s* will be
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|
|||
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|
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°8¼U¿_¹_Ô(MØô �H“ SÒ(Ù˜ %¨¨l¸c¸UÑ.BÕCñ Dr z Examining: rE )rW rà )rX rK rZ rN rW rL rY s ``` @@@r Ú print_cyclesr[ ¹ sM ý€ ó ô ÷.Dñ Dð. ò "ˆØ�‰˜+ c W¨BÐ/Ô0Ù��S˜"˜bÕ!ñ"r c óH — e Zd ZdZd d„Zd„ Zd„ Zd„ Zd„ Zd„ Z d„ Z
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d „ Z d
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„ Z y )
|
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|
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ÚGroupera#
|
|||
|
|
A disjoint-set data structure.
|
|||
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|
|
|||
|
|
Objects can be joined using :meth:`join`, tested for connectedness
|
|||
|
|
using :meth:`joined`, and all disjoint sets can be retrieved by
|
|||
|
|
using the object as an iterator.
|
|||
|
|
|
|||
|
|
The objects being joined must be hashable and weak-referenceable.
|
|||
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|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
>>> from matplotlib.cbook import Grouper
|
|||
|
|
>>> class Foo:
|
|||
|
|
... def __init__(self, s):
|
|||
|
|
... self.s = s
|
|||
|
|
... def __repr__(self):
|
|||
|
|
... return self.s
|
|||
|
|
...
|
|||
|
|
>>> a, b, c, d, e, f = [Foo(x) for x in 'abcdef']
|
|||
|
|
>>> grp = Grouper()
|
|||
|
|
>>> grp.join(a, b)
|
|||
|
|
>>> grp.join(b, c)
|
|||
|
|
>>> grp.join(d, e)
|
|||
|
|
>>> list(grp)
|
|||
|
|
[[a, b, c], [d, e]]
|
|||
|
|
>>> grp.joined(a, b)
|
|||
|
|
True
|
|||
|
|
>>> grp.joined(a, c)
|
|||
|
|
True
|
|||
|
|
>>> grp.joined(a, d)
|
|||
|
|
False
|
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$Ò 3ó5ˆŒ
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Ü�4‹jð
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c_ Y Γw xY w)zY
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D c ó€ — | j j |t « « | j j |« u S )z7Return whether *a* and *b* are members of the same set.)ra r/ Úobject©r rk Úbs r ÚjoinedzGrouper.joinedT s/ € à—
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×!Ñ! !¤V£XÓ.°$·-±-×2CÑ2CÀAÓ2FÐFÐGr c ó” — | j j ||h« j |« | j j |d« y)z>Remove *a* from the grouper, doing nothing if it is not there.N)ra rt Úremoverb ©r rk s r rx zGrouper.removeX s7 € à �
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¬« –— Œ' yc c}w w)z‰
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|||
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Iterate over each of the disjoint sets as a list.
|
|||
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|
|
|||
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|
The iterator is invalid if interleaved with calls to join().
|
|||
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©rk N)ra r; rN Úsortedrb rq )r ÚgroupÚ
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unique_groupss r rw zGrouper.__iter__] sh è ø€ ð 8<·}±}×7KÑ7KÓ7MÖN¨eœ˜E› EÑ)ÐNˆ
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ÐNØ"×)Ñ)Ó+ò @ˆEܘ D§N¡N×$>Ñ$>Ô?Ó ?ñ @ùò Oùs ‚ A2ŸA-²A A2c ó~ — | j j ||g« }t || j j ¬« S )z:Return all of the items joined with *a*, including itself.r{ )ra r/ r| rb )r rk Úsiblingss r Ú get_siblingszGrouper.get_siblingsg s1 € à—=‘=×$Ñ$ Q¨¨Ó,ˆÜ�h D§N¡N×$6Ñ$6Ô7Ð7r N)r )
|
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@ó8r r] c ó. — e Zd ZdZd„ Zd„ Zd„ Zd„ Zd„ Zy)Ú GrouperViewz!Immutable view over a `.Grouper`.c ó — || _ y r
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©Ú_grouper)r Úgroupers r r zGrouperView.__init__p s € ° ¥ r c ó — || j v S r
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)r† rv rt s r rv zGrouperView.joinedt s € ð �}‰}×#Ñ# A qÓ)Ð)r c ó8 — | j j |« S )zL
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|||
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)r† r� ry s r r� zGrouperView.get_siblingsz s € ð �}‰}×)Ñ)¨!Ó,Ð,r N) r r r r r ri rw rv r� r r r rƒ rƒ m s „ Ù+â8Ú>Ú2ò*ó -r rƒ c
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óª — | j t | « df« }t j t | « « |z }t j t | « dz
|
|||
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|||
|
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Resample an array with ``steps - 1`` points between original point pairs.
|
|||
|
|
|
|||
|
|
Along each column of *a*, ``(steps - 1)`` points are introduced between
|
|||
|
|
each original values; the values are linearly interpolated.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
a : array, shape (n, ...)
|
|||
|
|
steps : int
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
array
|
|||
|
|
shape ``((n - 1) * steps + 1, ...)``
|
|||
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|
|||
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|
Find all masked and/or non-finite points in a set of arguments,
|
|||
|
|
and return the arguments with only the unmasked points remaining.
|
|||
|
|
|
|||
|
|
Arguments can be in any of 5 categories:
|
|||
|
|
|
|||
|
|
1) 1-D masked arrays
|
|||
|
|
2) 1-D ndarrays
|
|||
|
|
3) ndarrays with more than one dimension
|
|||
|
|
4) other non-string iterables
|
|||
|
|
5) anything else
|
|||
|
|
|
|||
|
|
The first argument must be in one of the first four categories;
|
|||
|
|
any argument with a length differing from that of the first
|
|||
|
|
argument (and hence anything in category 5) then will be
|
|||
|
|
passed through unchanged.
|
|||
|
|
|
|||
|
|
Masks are obtained from all arguments of the correct length
|
|||
|
|
in categories 1, 2, and 4; a point is bad if masked in a masked
|
|||
|
|
array or if it is a nan or inf. No attempt is made to
|
|||
|
|
extract a mask from categories 2, 3, and 4 if `numpy.isfinite`
|
|||
|
|
does not yield a Boolean array.
|
|||
|
|
|
|||
|
|
All input arguments that are not passed unchanged are returned
|
|||
|
|
as ndarrays after removing the points or rows corresponding to
|
|||
|
|
masks in any of the arguments.
|
|||
|
|
|
|||
|
|
A vastly simpler version of this function was originally
|
|||
|
|
written as a helper for Axes.scatter().
|
|||
|
|
|
|||
|
|
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|||
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|
|||
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|
|||
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|
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|
|||
|
|
|
|||
|
|
Arguments can be in any of 5 categories:
|
|||
|
|
|
|||
|
|
1) 1-D masked arrays
|
|||
|
|
2) 1-D ndarrays
|
|||
|
|
3) ndarrays with more than one dimension
|
|||
|
|
4) other non-string iterables
|
|||
|
|
5) anything else
|
|||
|
|
|
|||
|
|
The first argument must be in one of the first four categories;
|
|||
|
|
any argument with a length differing from that of the first
|
|||
|
|
argument (and hence anything in category 5) then will be
|
|||
|
|
passed through unchanged.
|
|||
|
|
|
|||
|
|
Masks are obtained from all arguments of the correct length
|
|||
|
|
in categories 1, 2, and 4; a point is bad if masked in a masked
|
|||
|
|
array or if it is a nan or inf. No attempt is made to
|
|||
|
|
extract a mask from categories 2 and 4 if `numpy.isfinite`
|
|||
|
|
does not yield a Boolean array. Category 3 is included to
|
|||
|
|
support RGB or RGBA ndarrays, which are assumed to have only
|
|||
|
|
valid values and which are passed through unchanged.
|
|||
|
|
|
|||
|
|
All input arguments that are not passed unchanged are returned
|
|||
|
|
as masked arrays if any masked points are found, otherwise as
|
|||
|
|
ndarrays.
|
|||
|
|
|
|||
|
|
r r rš FrÊ r› ©r7 T©r« )r¦ r rØ ri rj rR r r< rœ r� Ú
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|||
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asanyarrayr rs r@ Ú is_maskedrŸ Ú
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|||
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logical_orr£ r6 )r r¦ r§ r¨ r© rm rË r« s r Ú_combine_masksr´ æ s™ € ô> ˆtŒ9Øܘ4 ™7Ô#ÜÐ<Ó=Ð=Ü ��Q‘‹L€EØ €E؈gœ˜D› Ñ!€GØ €Eܘ$“ó ‰ˆˆ1Ü ˜qÔ !¤S¨£V¨u¢_Ø �L‰L˜�Oä˜!œRŸU™U× .Ñ .Ô/°A·F±F¸Q²JÜ Ð!<Ó=Ð=ð
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|
|||
|
|
Broadcast inputs, combining all masked arrays.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
*args : array-like
|
|||
|
|
The inputs to broadcast.
|
|||
|
|
compress : bool, default: False
|
|||
|
|
Whether to compress the masked arrays. If False, the masked values
|
|||
|
|
are replaced by NaNs.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
list of array-like
|
|||
|
|
The broadcasted and masked inputs.
|
|||
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|
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Return a list of dictionaries of statistics used to draw a series of box
|
|||
|
|
and whisker plots using `~.Axes.bxp`.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
X : array-like
|
|||
|
|
Data that will be represented in the boxplots. Should have 2 or
|
|||
|
|
fewer dimensions.
|
|||
|
|
|
|||
|
|
whis : float or (float, float), default: 1.5
|
|||
|
|
The position of the whiskers.
|
|||
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|
|
|||
|
|
If a float, the lower whisker is at the lowest datum above
|
|||
|
|
``Q1 - whis*(Q3-Q1)``, and the upper whisker at the highest datum below
|
|||
|
|
``Q3 + whis*(Q3-Q1)``, where Q1 and Q3 are the first and third
|
|||
|
|
quartiles. The default value of ``whis = 1.5`` corresponds to Tukey's
|
|||
|
|
original definition of boxplots.
|
|||
|
|
|
|||
|
|
If a pair of floats, they indicate the percentiles at which to draw the
|
|||
|
|
whiskers (e.g., (5, 95)). In particular, setting this to (0, 100)
|
|||
|
|
results in whiskers covering the whole range of the data.
|
|||
|
|
|
|||
|
|
In the edge case where ``Q1 == Q3``, *whis* is automatically set to
|
|||
|
|
(0, 100) (cover the whole range of the data) if *autorange* is True.
|
|||
|
|
|
|||
|
|
Beyond the whiskers, data are considered outliers and are plotted as
|
|||
|
|
individual points.
|
|||
|
|
|
|||
|
|
bootstrap : int, optional
|
|||
|
|
Number of times the confidence intervals around the median
|
|||
|
|
should be bootstrapped (percentile method).
|
|||
|
|
|
|||
|
|
labels : list of str, optional
|
|||
|
|
Labels for each dataset. Length must be compatible with
|
|||
|
|
dimensions of *X*.
|
|||
|
|
|
|||
|
|
autorange : bool, optional (False)
|
|||
|
|
When `True` and the data are distributed such that the 25th and 75th
|
|||
|
|
percentiles are equal, ``whis`` is set to (0, 100) such that the
|
|||
|
|
whisker ends are at the minimum and maximum of the data.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
list of dict
|
|||
|
|
A list of dictionaries containing the results for each column
|
|||
|
|
of data. Keys of each dictionary are the following:
|
|||
|
|
|
|||
|
|
======== ===================================
|
|||
|
|
Key Value Description
|
|||
|
|
======== ===================================
|
|||
|
|
label tick label for the boxplot
|
|||
|
|
mean arithmetic mean value
|
|||
|
|
med 50th percentile
|
|||
|
|
q1 first quartile (25th percentile)
|
|||
|
|
q3 third quartile (75th percentile)
|
|||
|
|
iqr interquartile range
|
|||
|
|
cilo lower notch around the median
|
|||
|
|
cihi upper notch around the median
|
|||
|
|
whislo end of the lower whisker
|
|||
|
|
whishi end of the upper whisker
|
|||
|
|
fliers outliers
|
|||
|
|
======== ===================================
|
|||
|
|
|
|||
|
|
Notes
|
|||
|
|
-----
|
|||
|
|
Non-bootstrapping approach to confidence interval uses Gaussian-based
|
|||
|
|
asymptotic approximation:
|
|||
|
|
|
|||
|
|
.. math::
|
|||
|
|
|
|||
|
|
\mathrm{med} \pm 1.57 \times \frac{\mathrm{iqr}}{\sqrt{N}}
|
|||
|
|
|
|||
|
|
General approach from:
|
|||
|
|
McGill, R., Tukey, J.W., and Larsen, W.A. (1978) "Variations of
|
|||
|
|
Boxplots", The American Statistician, 32:12-16.
|
|||
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|
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|
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|
|
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|
|||
|
|
|
|||
|
|
Lists of iterables are converted by applying `numpy.asanyarray` to each of
|
|||
|
|
their elements. 1D ndarrays are returned in a singleton list containing
|
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|
|
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|
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|
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|
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|
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r rÊ )rÊ rÇ rÈ z must have 2 or fewer dimensionsTF)r rR r r¡ Ú transposer¦ r� rŽ rØ rï rŠ r[ r± rj )r× ÚnamerË ÚresultÚis_1dÚxiÚnds r rå rå ] si € ô ˜Ó€Aô �!”R—Z‘ZÔ Ø
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|
|||
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|
Return a list of dictionaries of data which can be used to draw a series
|
|||
|
|
of violin plots.
|
|||
|
|
|
|||
|
|
See the ``Returns`` section below to view the required keys of the
|
|||
|
|
dictionary.
|
|||
|
|
|
|||
|
|
Users can skip this function and pass a user-defined set of dictionaries
|
|||
|
|
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|
|||
|
|
to do the calculations. See the *Returns* section below for the keys
|
|||
|
|
that must be present in the dictionaries.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
X : array-like
|
|||
|
|
Sample data that will be used to produce the gaussian kernel density
|
|||
|
|
estimates. Must have 2 or fewer dimensions.
|
|||
|
|
|
|||
|
|
method : callable
|
|||
|
|
The method used to calculate the kernel density estimate for each
|
|||
|
|
column of data. When called via ``method(v, coords)``, it should
|
|||
|
|
return a vector of the values of the KDE evaluated at the values
|
|||
|
|
specified in coords.
|
|||
|
|
|
|||
|
|
points : int, default: 100
|
|||
|
|
Defines the number of points to evaluate each of the gaussian kernel
|
|||
|
|
density estimates at.
|
|||
|
|
|
|||
|
|
quantiles : array-like, default: None
|
|||
|
|
Defines (if not None) a list of floats in interval [0, 1] for each
|
|||
|
|
column of data, which represents the quantiles that will be rendered
|
|||
|
|
for that column of data. Must have 2 or fewer dimensions. 1D array will
|
|||
|
|
be treated as a singleton list containing them.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
list of dict
|
|||
|
|
A list of dictionaries containing the results for each column of data.
|
|||
|
|
The dictionaries contain at least the following:
|
|||
|
|
|
|||
|
|
- coords: A list of scalars containing the coordinates this particular
|
|||
|
|
kernel density estimate was evaluated at.
|
|||
|
|
- vals: A list of scalars containing the values of the kernel density
|
|||
|
|
estimate at each of the coordinates given in *coords*.
|
|||
|
|
- mean: The mean value for this column of data.
|
|||
|
|
- median: The median value for this column of data.
|
|||
|
|
- min: The minimum value for this column of data.
|
|||
|
|
- max: The maximum value for this column of data.
|
|||
|
|
- quantiles: The quantile values for this column of data.
|
|||
|
|
r× r Ú quantileszLList of violinplot statistics and quantiles values must have the same lengthrä ÚvalsÚcoordsrÚ rÆ r' r* )
|
|||
|
|
rå r¦ rØ rç r r' r* rÇ ÚlinspacerÚ rÆ r
|
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|
rj ) r× ÚmethodÚpointsr ÚvpstatsrË Úqrñ Úmin_valÚmax_valÚ quantile_valr s r Ú violin_statsr ™ sH € ðj €Gô �A�sÓ€Að Ð ¤ Y£ °1Ò!4Ü ¨;Ó7‰ ð �Dœ3˜q›6‘Mˆ ô ˆ1ƒv”�Y“ Ò Üð 6ó 7ð 7ô �a˜Ó#ò ‰ˆˆAàˆô —&‘&˜“)ˆÜ—&‘&˜“)ˆÜ—}‘} Q¨¨a©Ó0ˆ ô —‘˜W g¨vÓ6ˆÙ ˜q &Ó)ˆˆf‰
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|
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|
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Ü Ÿ)™) A›,ˆˆh‰Ø ˆˆe‰ Ø ˆˆe‰ Ü Ÿ]™]¨<Ó 8ˆˆkÑð �‰�uÕ ð- ð0 €Nr c óú — t j dt |« z t dt | « z dz
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d« f« }| |dddd…f< |dddd…f |dddd…f< ||dd…ddd…f< |dd…ddd…f |dd…ddd…f< |S )a(
|
|||
|
|
Convert continuous line to pre-steps.
|
|||
|
|
|
|||
|
|
Given a set of ``N`` points, convert to ``2N - 1`` points, which when
|
|||
|
|
connected linearly give a step function which changes values at the
|
|||
|
|
beginning of the intervals.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
x : array
|
|||
|
|
The x location of the steps. May be empty.
|
|||
|
|
|
|||
|
|
y1, ..., yp : array
|
|||
|
|
y arrays to be turned into steps; all must be the same length as ``x``.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
array
|
|||
|
|
The x and y values converted to steps in the same order as the input;
|
|||
|
|
can be unpacked as ``x_out, y1_out, ..., yp_out``. If the input is
|
|||
|
|
length ``N``, each of these arrays will be length ``2N + 1``. For
|
|||
|
|
``N=0``, the length will be 0.
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
>>> x_s, y1_s, y2_s = pts_to_prestep(x, y1, y2)
|
|||
|
|
rÊ rÇ r Néþÿÿÿ©r Úzerosr¦ r* ©rË r r” s r Úpts_to_prestepr% û s¨ € ô8
|
|||
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�H‰H�aœ#˜d›)‘m¤S¨¬S°«V©°a© ¸Ó%;Ð<Ó =€Eð €Eˆ!ˆQˆT�ˆTˆ'�Nؘ1˜a 1˜f˜9Ñ%€Eˆ!ˆQˆT�ˆTˆ'�NØ€Eˆ!‰"ˆaˆd�ˆdˆ(�OؘA™B 1 ˜H‘o€Eˆ!‰"ˆaˆd�ˆdˆ(�OØ €Lr c óú — t j dt |« z t dt | « z dz
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|||
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d« f« }| |dddd…f< |dddd…f |dddd…f< ||dd…ddd…f< |dd…ddd…f |dd…ddd…f< |S )a#
|
|||
|
|
Convert continuous line to post-steps.
|
|||
|
|
|
|||
|
|
Given a set of ``N`` points convert to ``2N + 1`` points, which when
|
|||
|
|
connected linearly give a step function which changes values at the end of
|
|||
|
|
the intervals.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
x : array
|
|||
|
|
The x location of the steps. May be empty.
|
|||
|
|
|
|||
|
|
y1, ..., yp : array
|
|||
|
|
y arrays to be turned into steps; all must be the same length as ``x``.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
array
|
|||
|
|
The x and y values converted to steps in the same order as the input;
|
|||
|
|
can be unpacked as ``x_out, y1_out, ..., yp_out``. If the input is
|
|||
|
|
length ``N``, each of these arrays will be length ``2N + 1``. For
|
|||
|
|
``N=0``, the length will be 0.
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
>>> x_s, y1_s, y2_s = pts_to_poststep(x, y1, y2)
|
|||
|
|
rÊ rÇ r Nr! r" r$ s r Úpts_to_poststepr' ! s¦ € ô8
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|
|||
|
|
Convert continuous line to mid-steps.
|
|||
|
|
|
|||
|
|
Given a set of ``N`` points convert to ``2N`` points which when connected
|
|||
|
|
linearly give a step function which changes values at the middle of the
|
|||
|
|
intervals.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
x : array
|
|||
|
|
The x location of the steps. May be empty.
|
|||
|
|
|
|||
|
|
y1, ..., yp : array
|
|||
|
|
y arrays to be turned into steps; all must be the same length as
|
|||
|
|
``x``.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
array
|
|||
|
|
The x and y values converted to steps in the same order as the input;
|
|||
|
|
can be unpacked as ``x_out, y1_out, ..., yp_out``. If the input is
|
|||
|
|
length ``N``, each of these arrays will be length ``2N``.
|
|||
|
|
|
|||
|
|
Examples
|
|||
|
|
--------
|
|||
|
|
>>> x_s, y1_s, y2_s = pts_to_midstep(x, y1, y2)
|
|||
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|
rÊ rÇ NrÈ r )r r# r¦ r± r$ s r Úpts_to_midstepr) E sÜ € ô8
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|
|||
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|
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|
|||
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|
|
|||
|
|
This is used for plotting (x, y) if x values are not explicitly given.
|
|||
|
|
|
|||
|
|
First try ``y.index`` (assuming *y* is a `pandas.Series`), if that
|
|||
|
|
fails, use ``range(len(y))``.
|
|||
|
|
|
|||
|
|
This will be extended in the future to deal with more types of
|
|||
|
|
labeled data.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
y : float or array-like
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
x, y : ndarray
|
|||
|
|
The x and y values to plot.
|
|||
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|||
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|
|||
|
|
|
|||
|
|
This is a method for internal use.
|
|||
|
|
|
|||
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|||
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Helper function to normalize kwarg inputs.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
kw : dict or None
|
|||
|
|
A dict of keyword arguments. None is explicitly supported and treated
|
|||
|
|
as an empty dict, to support functions with an optional parameter of
|
|||
|
|
the form ``props=None``.
|
|||
|
|
|
|||
|
|
alias_mapping : dict or Artist subclass or Artist instance, optional
|
|||
|
|
A mapping between a canonical name to a list of aliases, in order of
|
|||
|
|
precedence from lowest to highest.
|
|||
|
|
|
|||
|
|
If the canonical value is not in the list it is assumed to have the
|
|||
|
|
highest priority.
|
|||
|
|
|
|||
|
|
If an Artist subclass or instance is passed, use its properties alias
|
|||
|
|
mapping.
|
|||
|
|
|
|||
|
|
Raises
|
|||
|
|
------
|
|||
|
|
TypeError
|
|||
|
|
To match what Python raises if invalid arguments/keyword arguments are
|
|||
|
|
passed to a callable.
|
|||
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r )ÚArtistÚ
|
|||
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|||
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|
|||
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|
Context manager for locking a path.
|
|||
|
|
|
|||
|
|
Usage::
|
|||
|
|
|
|||
|
|
with _lock_path(path):
|
|||
|
|
...
|
|||
|
|
|
|||
|
|
Another thread or process that attempts to lock the same path will wait
|
|||
|
|
until this context manager is exited.
|
|||
|
|
|
|||
|
|
The lock is implemented by creating a temporary file in the parent
|
|||
|
|
directory, so that directory must exist and be writable.
|
|||
|
|
z.matplotlib-lockrâ gš™™™™™¹?ÚxbNzÛLock error: Matplotlib failed to acquire the following lock file:
|
|||
|
|
{}
|
|||
|
|
This maybe due to another process holding this lock file. If you are sure no
|
|||
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other Matplotlib process is running, remove this file and try again.) r Ú with_namer Úrangerò ÚFileExistsErrorÚtimeÚsleepÚ TimeoutErrorÚformatÚunlink)r Ú lock_pathÚretriesÚ sleeptimeÚ_s r Ú
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|||
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|||
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|
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|||
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|||
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|
B ÂBÂCÂ B;Â8CÂ:B;Â;CÂ>CÃCÚzorderr{ c ó$ — |t | « « S )a
|
|||
|
|
Get the topmost artist of a list.
|
|||
|
|
|
|||
|
|
In case of a tie, return the *last* of the tied artists, as it will be
|
|||
|
|
drawn on top of the others. `max` returns the first maximum in case of
|
|||
|
|
ties, so we need to iterate over the list in reverse order.
|
|||
|
|
)Úreversed)ÚartistsÚ _cached_maxs r Ú_topmost_artistre 7 s € ñ ”x Ó(Ó )Ð)r c ó0 — t | t « xr | |k( S )a
|
|||
|
|
Return whether *obj* is a string equal to string *s*.
|
|||
|
|
|
|||
|
|
This helper solely exists to handle the case where *obj* is a numpy array,
|
|||
|
|
because in such cases, a naive ``obj == s`` would yield an array, which
|
|||
|
|
cannot be used in a boolean context.
|
|||
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)rR rï ©rN rŠ s r Ú
|
|||
|
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|
|||
|
|
Return whether *obj* is a string equal, when lowercased, to string *s*.
|
|||
|
|
|
|||
|
|
This helper solely exists to handle the case where *obj* is a numpy array,
|
|||
|
|
because in such cases, a naive ``obj == s`` would yield an array, which
|
|||
|
|
cannot be used in a boolean context.
|
|||
|
|
)rR rï r
|
|||
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rg s r Ú_str_lower_equalrj O s! € ô �cœ3Ó Ò 4 C§I¡I£K°1Ñ$4Ð4r c ó® — t j dd }t j ddd… }t j | d|f | |df | d|f | |df f« S )aZ
|
|||
|
|
Get the elements on the perimeter of *arr*.
|
|||
|
|
|
|||
|
|
Parameters
|
|||
|
|
----------
|
|||
|
|
arr : ndarray, shape (M, N)
|
|||
|
|
The input array.
|
|||
|
|
|
|||
|
|
Returns
|
|||
|
|
-------
|
|||
|
|
ndarray, shape (2*(M - 1) + 2*(N - 1),)
|
|||
|
|
The elements on the perimeter of the array::
|